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ICU Admission Score for Predicting Morbidity and Mortality Risk After Coronary Artery Bypass Grafting
ICU Admission Score for Predicting Morbidity and Mortality Risk After Coronary Artery Bypass Grafting Thomas L. Higgins, MD, Fawzy G. Estafanous, MD, Floyd D. Loop, MD, Gerald J. Beck, PhD, Jar-Chi Lee, MS, Norman J. Starr, MD, William A. Knaus, MD, and Delos M. Cosgrove III, MD Departments of Cardiothoracic Anesthesia, Thoracic and Cardiovascular Surgery, and Biostatistics and Epidemiology, The Cleveland Clinic Foundation, Cleveland, Ohio
Background. This study was performed to develop an intensive care unit (ICU) admission risk score based on preoperative condition and intraoperative events. This score provides a tool with which to judge the effects of ICU quality of care on outcome. Methods. Data were collected prospectively on 4,918 patients (study group n 5 2,793 and a validation data set n 5 2,125) undergoing coronary artery bypass grafting alone or combined with a valve or carotid procedure between January 1, 1993, and March 31, 1995. Data were analyzed by univariate and multiple logistic regression with the end points of hospital mortality and serious ICU morbidity (stroke, low cardiac output, myocardial infarction, prolonged ventilation, serious infection, renal failure, or death).
Results. Eight risk factors predicted hospital mortality at ICU admission, and these factors and five others predicted morbidity. A clinical score, weighted equally for morbidity and mortality, was developed. All models fit according to the Hosmer-Lemeshow goodness-of-fit test. This score applies equally well to patients undergoing isolated coronary artery bypass grafting. Conclusions. This model is complementary to our previously reported preoperative model, allowing the process of ICU care to be measured independent of the operative care. Sequential scoring also allows updated prognoses at different points in the continuum of care.
O
Material and Methods
utcome after coronary artery bypass grafting (CABG) is determined by the preoperative status of the patient, as well as by technical factors in the operating room and intensive care unit (ICU). Preoperative risk stratification in the cardiac surgical population consistently identifies advanced age, emergency status, left ventricular dysfunction, a previous heart operation, diabetes, female sex, small body size, and renal disease as factors that affect outcome [1–5]. However, morbidity and mortality after CABG are also influenced by surgical and anesthetic techniques [6, 7], including time on cardiopulmonary bypass [7], completeness of revascularization [8], efficacy of myocardial protection [9], hemodynamic management [10], and unforeseen events in the operating room. The patient’s prognosis at arrival to the ICU may differ from the preoperative prognosis. In the present study, we evaluated the relative contribution of preoperative condition, operating room events, and physiologic measurements at ICU admission to outcome and describe a risk stratification score based on a patient’s status at ICU admission. Accepted for publication April 7, 1997. Address reprint requests to Dr Estafanous, Division of Anesthesiology and Critical Care, The Cleveland Clinic Foundation, 9500 Euclid Ave, Cleveland, OH 44195.
© 1997 by The Society of Thoracic Surgeons Published by Elsevier Science Inc
(Ann Thorac Surg 1997;64:1050 – 8) © 1997 by The Society of Thoracic Surgeons
We prospectively collected data on 4,918 consecutive adult patients undergoing CABG between January 1, 1993, and March 31, 1995. Preoperative data were collected by resident physicians, and ICU admission data were collected by trained physician assistants. Data collected during the first 15 months (n 5 2,793) were used to develop the model, and data collected during the next 12 months (n 5 2,125) were used to validate the model. Patients undergoing repeat operation, simultaneous carotid endarterectomy, or repair or replacement of the mitral or aortic valve (or both) were included in the study, because these “combined” CABG patients represent a large (27%) and growing part of our surgical population. A separate analysis was also conducted on the subset of patients (n 5 2,035; 73% of the developmental data set) undergoing isolated coronary revascularization. Six patients underwent both a primary and a repeat revascularization procedure during the study period, but only the first operation was considered for analysis. The reliability of data collection was assessed by comparing the data with chart review of a random subset. This review showed a relevant agreement of 98% to 100% of patients for each variable used in the final models. We chose more than 100 risk factors from the literature, our clinical experience, and our own work [1, 8, 11] and 0003-4975/97/$17.00 PII S0003-4975(97)00553-5
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examined the influence of each on outcome at discharge. Emergency cases were those characterized by unstable angina, unstable hemodynamics, or ischemic valvular dysfunction that could not be controlled medically. Left ventricular function was categorized as normal (ejection fraction [EF] $0.60], mildly impaired (EF 0.50 to 0.59), mildly to moderately impaired (EF 0.46 to 0.49), moderately impaired (EF 0.41 to 0.45), moderately to severely impaired (EF 0.36 to 0.40), or severely impaired (EF #0.35) based on the cardiologist’s evaluation of the ventriculogram at cardiac catheterization or by echocardiography if a ventriculogram was not recorded. Diabetes or chronic obstructive pulmonary disease was diagnosed only if the patient had a history of the condition and was maintained on appropriate medications. Valvular pathology was evaluated by stenosis versus regurgitation and repair versus replacement. Cardiopulmonary bypass time was the total of all bypass runs if a second or subsequent period of bypass was conducted. The need for a second or greater bypass run was also considered as a separate factor in the analysis. Morbidity was defined as the presence of one or more of the following during hospitalization: (1) cardiac complication: low cardiac output (sustained cardiac index #1.8 L z min21 z m22 despite adequate preload and inotropic support) or myocardial infarction documented by electrocardiography and enzyme criteria and that required the use of an intraaortic balloon pump (IABP) or a ventricular assist device (IABP and assist devices were resorted to when vasoactive drug support failed to achieve a cardiac index .2 L/m or mean arterial pressure .60 mm Hg without significant side effects (eg, persistent ischemia, arrhythmias); (2) prolonged ventilatory support: mechanical ventilation for 72 hours or more; (3) central nervous system complication: focal brain lesion confirmed by clinical findings, computed tomographic scan, or both, or diffuse encephalopathy with more than 24 hours of severely altered mental status, failure to awaken postoperatively, or both; (4) renal failure: urine output of ,400 mL/24 hours, institution of dialysis, or both; (5) serious infection: culture-proven pneumonia (blood or sputum), mediastinitis, wound infection, sepsis syndrome, or septic shock; or (6) death, because early hospital death might preclude the diagnosis of other morbidities. Mortality included all deaths during the hospitalization for the operation, regardless of length of stay.
Development of the Logistic Regression Model The association of each factor with morbidity and mortality was evaluated using a x2 test or Fisher’s exact test for categoric variables and Student’s t test for continuous variables. Because of the large number of variables analyzed, only the most significant are presented in Tables 1 and 2. Continuous variables significantly related to outcome were plotted using a locally weighted smoothing scatterplot technique [12] to assess linearity on the logit scale. If the relationship to risk was linear, the factor was entered into the model as a continuous variable. If the relationship to risk was not linear, appropriate
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cut points were determined from the locally weighted smoothing scatterplots to divide the distribution of values into categories. Locally weighted smoothing scatterplots were also used to divide variables that were continuous in the logistic models into categories for use in a clinical risk stratification score (see below). Factors considered for the multiple logistic regression included only those that were significant (p , 0.05) by univariate analysis, had a prevalence of at least 2% for categoric variables, and that indicated a patient condition rather than a therapeutic decision. Interactions of potentially related variables, such as low cardiac index and the use of an IABP, were also evaluated. The logistic regression analyses used a forward stepwise selection procedure with p values of 0.15 to enter and 0.05 to remain in the model. Two models were developed: one each for mortality and morbidity. The number of terms allowed in a logistic model was limited to 10% of the number of outcome events, with only the most significant factors included to avoid overfitting the models [13]. The goodness of fit of each final logistic model was evaluated using the Hosmer-Lemeshow x2 statistic [14]. In the event of a low number of expected events, the lowest deciles were combined for statistical analysis, with appropriate reduction in the degrees of freedom. Receiver operating characteristic curves were used to generate a C-statistic (area under the curve) to measure and compare the accuracy of the models [15, 16]. C-statistic values closer to 1.0 indicate better discrimination by the model.
Development of the Clinical Model and Risk Stratification Score A clinical model was created by first categorizing continuous predictors in the logistic regression models by using the locally weighted smoothing scatterplot procedure to identify cut points and then refitting the logistic models using all predictors as categoric variables. For example, for cardiopulmonary bypass time the cut point of 160 minutes was based on the much higher mortality above this value in the locally weighted smoothing scatterplots. Cut points were not based on being one or two standard deviations above the mean. Then a numeric score was calculated by summing the regression coefficient estimates of the logistic models for morbidity and mortality, multiplying by 2, and then rounding to the nearest integer to produce a clinical model that equally weights mortality and morbidity. This clinical model was evaluated for its goodness of fit and discrimination on the developmental data set, the subset of patients who underwent isolated coronary bypass without other simultaneous procedures, and the validation data set. Individual levels of morbidity and mortality risk were then combined into five levels of risk to facilitate the application of the scoring system when smaller numbers of patients are available, as might be seen in quarterly analysis or at institutions with smaller surgical volumes. The observed values in the validation set were then compared with the 95% confidence intervals of the predicted values generated from the developmental data set.
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Table 1. Selected Categoric Variables and Univariate Analysis Variable Sex (female) History of COPD/asthma History of pulmonary hypertension History of congestive heart failure History of dysrhythmias History of vascular disease or operation History of carotid disease or CVA Left main stenosis $70% Endocarditis Preop IABP Recent myocardial infarction (,3 mo) Diabetes mellitus (requiring medication) One prior heart operation Two or more prior heart operations Preop LVEF ,0.35 Emergency procedure One or two IMA grafts IABP after bypass AV replacement or repair MV replacement MV repair TV replacement or repair Aortic operation in addition to CABG OR HR .100 beats/min OR CI ,2.1 L z min21 z m22 Transfused more than 2 units in OR Urine output in OR ,500 mL More than one CPB run More than one cross-clamp ICU admit HR .100 beats/min ICU admit MAP ,60 mm Hg ICU admit CVP .17 mm Hg ICU admit PA diastolic .17 mm Hg ICU admit CI ,2.1 L z min21 z m22 ICU admit inotrope ICU admit pressor ICU admit antiarrhythmic ICU arrival MAP ,70 mm Hg
Mortality
Morbidity
Occurrence (%)
Odds Ratio
p Value
Odds Ratio
p Value
23.5 7.4 1.9 19.0 16.8 11.3 17.0 7.4 0.2 2.7 21.4 9.7 20.1 2.9 6.3 4.2 65.6 4.4 8.8 3.2 4.2 0.5 2.0 3.7 19.7 51.1 3.6 5.9 2.4 25.6 1.3 4.4 25.0 16.7 49.8 20.2 11.9 12.6
1.63 1.77 1.14 3.38 2.40 2.28 1.68 1.95 5.98 7.99 1.76 2.39 1.72 2.83 3.14 5.07 0.14 9.40 1.95 4.62 1.98 13.73 5.32 7.05 3.36 5.72 2.02 6.49 1.92 4.20 6.48 4.15 3.54 2.89 8.60 3.93 3.50 2.80
0.030 0.079 0.855 0.0001 0.0001 0.001 0.034 0.033 0.064 0.0001 0.013 0.001 0.022 0.014 0.0001 0.0001 0.0001 0.0001 0.023 0.0001 0.086 0.0001 0.0001 0.0001 0.0001 0.0001 0.100 0.0001 0.207 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
1.75 1.47 2.25 2.28 1.64 1.9 1.79 1.93 4.33 8.16 1.30 1.85 2.17 3.62 3.51 3.23 0.32 13.10 1.68 4.27 1.40 7.52 3.92 3.90 2.19 3.16 1.70 5.76 3.86 3.05 2.49 4.08 3.32 1.71 5.35 4.14 3.04 1.66
0.0001 0.068 0.015 0.0001 0.001 0.0001 0.0001 0.001 0.066 0.0001 0.067 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.006 0.0001 0.222 0.0001 0.0001 0.0001 0.0001 0.0001 0.057 0.0001 0.0001 0.0001 0.024 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.002
AV 5 atrial valve; CABG 5 coronary artery bypass grafting; CI 5 cardiac index; COPD 5 chronic obstructic pulmonary disease; CPB 5 cardiopulmonary bypass; CVA 5 cerebrovascular accident; CVP 5 central venous pressure; HR 5 heart rate; IABP 5 intraaortic balloon pump; ICU 5 intensive care unit; IMA 5 internal mammary artery; LVEF 5 left ventricular ejection fraction; MAP 5 mean arterial pressure; MV 5 mitral valve; PA 5 pulmonary artery pressure; OR 5 operating room; TV 5 tricuspid valve.
Most statistical analyses were performed with SAS version 6.0 (Statistical Analysis Systems, Cary, NC). The receiver operating characteristic analyses used programs developed by Metz and associates [15, 16].
Results Univariate risk factors for morbidity and mortality are presented in Table 1 (categoric variables) and Table 2 (continuous variables). Selected variables that were not statistically significant by univariate analysis included low urine output in the operating room, history of
chronic obstructive pulmonary disease requiring medication, history of renal failure requiring dialysis, chronic hypertension, race other than white, prior carotid operation, prior lung operation, and preoperative use of antiarrhythmics or calcium channel antagonists. Ventricular aneurysectomy or carotid operation in addition to CABG also did not achieve statistical significance. Fifty-eight variables qualified for entry into the multiple logistic regression analysis. The logistic regression models for mortality and morbidity are presented in Tables 3 and 4. These models were fit on 2,440 patients having complete data on these variables. To avoid over-
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Table 2. Selected Continuous Variables: Univariate Analysis Variable Age (y) Preop hematocrit (%) Preop creatinine (mg/dL) Preop albumin (mg/dL) Preop bilirubin (mg/dL) Height (cm) Weight (kg) Body surface area (Dubois method; m2) Preop arterial PCO2 (mm Hg) Preoperative cardiac index (L z min21 z m22) Cross clamp time (total minutes) CBP time (total minutes) Urine output in OR (mL z kg21 z h21) ICU arrival FIO2 ICU arrival pH (arterial blood gas, units) ICU arrival PCO2 (arterial blood gas, mm Hg) ICU arrival PO2 (arterial blood gas, mm Hg) ICU arrival HCO3 (arterial blood gas, mm Hg) ICU arrival core temp (degrees) ICU arrival HR (beats/min) ICU arrival MAP (mm Hg) ICU arrival CVP (mm Hg) ICU arrival PA systolic (mm Hg) ICU arrival PA diastolic (mm Hg) ICU arrival CI (L z min21 z m22) ICU arrival A-a O2 gradient (mm Hg) ICU hematocrit (%) ICU K1 (mEq/L) ICU glucose (mg/dL) ICU WBC (/mL)
Mean Value
SD
Mortality p Value
64.3 40.9 1.27 4.14 0.66 170.7 81.6 1.93 39.0 2.65 70.1 109.1 4.42 0.61 7.42 35.8 158.2 23.4 36.2 93.5 82.2 10.5 29.7 14.7 2.68 229.5 29.6 3.76 206.5 12,100.0
10.0 4.8 0.65 0.48 0.40 9.4 16.1 0.21 4.80 0.64 30.8 47.0 2.57 0.08 0.06 5.4 60.7 2.3 0.7 14.3 11.5 3.8 8.1 5.0 0.63 74.8 4.3 0.51 76.6 4,810.0
0.0001 0.0004 0.005 0.0001 0.005 0.038 0.0089 0.004 0.79 0.0001 0.0008 0.0001 0.011 0.0009 0.0001 0.51 0.82 0.0001 0.007 0.0001 0.0124 0.0001 0.0001 0.0001 0.0002 0.003 0.002 0.11 0.0001 0.027
Morbidity p Value 0.0001 0.0001 0.005 0.0001 0.008 0.0002 0.0001 0.0001 0.50 0.0001 0.0001 0.0001 0.035 0.0001 0.0001 0.0023 0.016 0.0001 0.11 0.0001 0.19 0.0001 0.0001 0.0001 0.006 0.0001 0.004 0.028 0.0001 0.003
A-a O2 gradient 5 alveolar-arterial oxygen gradient; CI 5 cardiac index; CPB 5 cardiopulmonary bypass; CVP 5 central venous HR 5 heart rate; ICU 5 intensive care unit; MAP 5 mean arterial pressure; OR 5 pressure; FiO2 5 fraction of inspired oxygen; PO2 5 oxygen tension; SD 5 standard deviation; operating room; PA 5 pulmonary artery pressure; PCO2 5 carbon dioxide tension; WBC 5 white blood cell count.
fitting, the mortality model was restricted to the first eight significant factors, because there were only 78 deaths. Significant factors that would have been entered
into the model without this restriction were central venous pressure and mean arterial pressure values at ICU admission, and a history of congestive heart failure.
Table 3. Intensive Care Unit Admission Mortality Model Variable Intercept Use of IABP After CPB Cardiac index at ICU admission Age per decade Preoperative serum albumin (mg/dL) History of peripheral vascular disease or interventions CPB time per 10 minutes HR .100 beats/min at ICU admission Arterial bicarbonate at ICU Admission (m/mol) a
Regression Coefficient
Standard Error
Odds Ratio
95% Confidence Interval
4.12 1.49 20.64a 0.39 21.74a 1.07 0.10 0.82 20.17a
2.10 0.35 0.22 0.15 0.24 0.31 0.02 0.27 0.05
... 4.46 0.53 1.49 0.18 2.91 1.11 2.27 0.85
... 2.23– 8.90 0.35– 0.80 1.11–2.00 0.11– 0.28 1.57–5.38 1.07–1.15 1.36 –3.85 0.76 – 0.94
Negative values in the regression coefficient imply that lower values of the variable increase mortality.
CPB 5 cardiopulmonary bypass;
HR 5 heart rate;
IABP 5 intraaortic balloon pump;
ICU 5 intensive care unit.
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Table 4. Intensive Care Unit Admission Morbidity Model Variable Intercept Body surface area (m2) .2 prior heart operations History of peripheral vascular disease or interventions Age per decade Preoperative serum creatinine (mg/dL) Preoperative serum albumin (mg/dL) CPB time per 10 minutes Use of IABP after CPB A-a gradient at ICU admission per 10 mm Hg HR .100 beats/min at ICU admission Cardiac index at ICU admission (L z min21 z m22) CVP at ICU admission (mm Hg) Arterial bicarbonate at ICU admission (mmol/L) a
Regression Coefficient
Standard Error
Odds Ratio
95% Confidence Interval
2.57 20.90* 0.80 0.72
1.53 0.38 0.32 0.20
... 0.41 2.23 2.06
... 0.19 – 0.86 1.18 – 4.22 1.39 –3.05
0.21 0.21 20.90* 0.07 1.96 0.04 0.49 20.47a 0.10 20.13a
0.09 0.08 0.15 0.02 0.25 0.01 0.16 0.13 0.02 0.03
1.23 1.24 0.41 1.07 7.11 1.04 1.64 0.63 1.10 0.88
1.04 –1.46 1.06 –1.44 0.30 – 0.55 1.03–1.12 4.34 –11.65 1.02–1.06 1.20 –2.23 0.49 – 0.80 1.06 –1.15 0.82– 0.93
Negative values in the regression coefficient imply that lower values of the variable increase morbidity.
A-a 5 alveolar-arterial; CPB 5 cardiopulmonary bypass; pump; ICU 5 intensive care unit.
CVP 5 central venous pressure;
No such limits applied to the morbidity model, which has 13 factors, including all eight factors in the mortality model, plus central venous pressure and alveolar–arterial gradient at ICU admission, preoperative serum creatinine level, reoperation status, and body surface area. The risk stratification score (Table 5) incorporates all factors affecting morbidity and mortality from the multiple logistic regression models, divides continuous variables into categories, and assigns integer point values of 1 to 7 on the basis of the logistic regression coefficients. A patient’s stratification score is obtained by adding the point values from Table 5, which can then be related to mortality (Fig 1) and morbidity (Fig 2). In the total population, the mortality rate was 3.1% and the morbidity rate was 10.4%, but rates vary by ICU risk stratification score. The categorization of risk levels was made on the basis of similar outcomes. For example, patients with a score less than 5 at ICU admission are at risk of less than 1% for mortality and less than 5% for morbidity. The risks of mortality and morbidity increase with higher scores (see Figs 1, 2). Receiver operating characteristic curves were generated for all models. The C-statistics (areas under the curves) reflecting mortality for the logistic regression mortality and clinical models were 0.86 and 0.87, respectively, which were not significantly different from each other. The C-statistics reflecting morbidity for the logistic regression morbidity and clinical model were 0.82 and 0.80, respectively, reflecting slightly better discrimination (p 5 0.02) for the logistic regression model. Applying the clinical model to patients undergoing isolated CABG generated a C-statistic of 0.87 for mortality and 0.82 for morbidity. Using the clinical model, observed outcomes in patients in the validation data set fell within the 95% confidence intervals predicted by the developmental data set (see Figs 1, 2). Applying the clinical model to
HR 5 heart rate;
IABP 5 intraaortic balloon
patients in the validation data set produced C-statistics of 0.85 for mortality and 0.82 for morbidity. HosmerLemeshow goodness of fit determined that all logistic and clinical models calibrate well. The accuracy and goodness of fit of both the logistic and clinical models were very similar indicating that the clinical severity score, although not a probability, can be useful in categorizing risks of mortality and morbidity in CABG patients.
Comment Preoperatively, patients undergoing open heart operations should be expected to be concerned about the possible outcome of the operation, including morbidity and mortality. Payors and health providers need similar information. Several preoperative risk status models have been developed to predict outcome after coronary artery operations [1–5]. These models all predict mortality, and possibly morbidity, with good accuracy but they also have shortcomings [17]. The patient’s physiologic response to the operation and anesthesia cannot always be predicted from preoperative information. Unexpected intraoperative events, management of anesthesia [10], efficacy of myocardial preservation [9], completeness of myocardial revascularization, and uneventful reoperation can neutralize or amplify preoperative risk. All these factors can affect the prognosis. Thus reevaluation and assessment of a patient’s condition at ICU admission complements the preoperative evaluation and allows for a proactive ICU management plan and better prediction of outcome. Previous work from our group [1] identified 13 preoperative risk factors for morbidity and mortality after CABG. Four of these factors are also significant in the ICU admission models: preoperative serum creatinine
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Table 5. Intensive Care Unit Risk Stratification Score Variable Preoperative Factors Small body size (BSA ,1.72 m2) Prior heart operation One Two or more History of operation or angioplasty for peripheral vascular disease Age $70 years Preoperative creatinine $1.9 mg/dL Preoperative albumin ,3.5 mg/dL Intraoperative factors CPB time $160 minutes Use of IABP after CPB ICU admission physiology A-a O2 gradient $ 250 mm Hg Heart rate $ 100 beats/min Cardiac index ,2.1 L z min21 z m22 CVP $17 mm Hg Arterial bicarbonate ,21 mmol/L
Value 1 1 2 3 3 4 5 3 7 2 3 3 4 4
A-a 5 alveolar-arterial; BSA 5 body surface area; CPB 5 cardiopulmonary bypass; CVP 5 central venous pressure; IABP 5 intraaortic balloon pump; ICU 5 intensive care unit.
level, age, prior cardiac operation, and a history of vascular disease. Data on serum albumin level, a significant factor in the ICU model, were not available for consideration in our preoperative risk model (Table 6). However, preoperative hematocrit, used previously as a measure of chronic disease, appears to account for some of the same infor-
Fig 1. Mortality by intensive care unit (ICU) admission risk group. Risk is estimated by summing the individual elements in Table 5. Results are displayed for the developmental set (1993–94) and the prospective validation set (1994 –95). All observed (validation) values fall within the 95% confidence intervals established from the developmental set.
Fig 2. Morbidity by intensive care unit (ICU) admission risk group. See legend for Figure 1.
mation as albumin (Pearson correlation coefficient between hematocrit and serum albumin 5 0.43). If serum albumin level is not considered as a variable, hematocrit would appear as a fifth factor common to the preoperative and ICU admission models. Several other factors in the preoperative risk score did not appear in the ICU admission score because these preoperative risk factors are managed during the operation or are subsumed by other scoring elements. For example, preoperative left ventricular dysfunction is an important preoperative risk factor [1–5, 18 –20] and it appears as a univariate risk factor in the present study, but it did not remain in the ICU admission logistic regression models. Preoperative ventricular dysfunction (EF , 0.35) is not associated with low cardiac index on arrival to ICU (p 5 0.50), but it is highly associated (p , 0.0001) with the use of IABP after bypass. The use of IABP after bypass thus becomes a better prognostic indicator than preoperative left ventricular dysfunction. This study also confirmed that intraoperative events such as cardiopulmonary bypass time and the use of an IABP are as important as preoperative risk factors in predicting outcome. Cardiopulmonary bypass time was analyzed as a continuous factor in the logistic model and it showed increase in mortality and morbidity with increasing time. We use 160 minutes as the cutpoint, which is 1.58 times the median time (101 minutes). The 160 minutes represents the 89.1 percentile and was chosen based on locally weighted smoothing scatterplot curve analysis demonstrating a sharp increase in poor outcome above this value. Emergency procedures are significant preoperative predictors of poor outcome, but were not significant in the ICU admission logistic regression models. Emergency patients are more likely to have other risk factors on ICU arrival such as low cardiac index, decreased serum albumin, higher alveolar–arterial oxygen gradient, elevated central venous pressure, and tachycardia. These
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Table 6. Preoperative Severity Score Variable Emergency Serum creatinine $1.9 mg% Serum creatinine 1.6 –1.8 mg% Severe LV dysfunction Prior cardiac operation Operative mitral valve insufficiency Age 75 years or older Age 65 to 74 years Prior vascular surgery COPD on medication Anemia (Hct #34%) Operative aortic stenosis Weight #65 kg Diabetes on medication Cerebrovascular disease Maximum score Clinically relevant range COPD 5 chronic obstructive pulmonary disease; LV 5 left ventricular.
Score 6 4 1 3 3 3 2 1 2 2 2 1 1 1 1 31 0 to 131 Hct 5 hematocrit;
Reprinted with permission from JAMA 1992;267:2345 (copyright 1992, American Medical Association).
were included in the logistic and clinical models. It is clear that scoring based on physiologic variables is more patient specific and is preferable to the term “emergency,” a definition that might vary between institutions. Several other physiologic measurements at ICU admission are predictors of outcome in this study, namely alveolar–arterial oxygen gradient, heart rate, cardiac index, central venous pressure, and arterial bicarbonate. The use of physiologic measurements to predict outcome was pioneered by the APACHE prognostic system [21] and successfully predicts mortality, resource use, and ICU length of stay in CABG patients [22]. The present study confirms the importance of physiologic measurements at ICU admission as outcome predictors. Some of the physiologic variables (such as body temperature and hematocrit) scored by APACHE-III, a benchmark for determining outcome in noncardiac intensive care units, may have a different relationship with outcome in CABG patients [21]. During cardiopulmonary bypass most patients are cooled systematically and their temperature at ICU admission can be less than 37°C. This does not reflect a pathologic status. Hemodilution is now a routine practice in cardiac surgery. Immediate postoperative hematocrit of 24% or greater is generally considered adequate. Experimental studies by our group [23] and our clinical experience demonstrated that hematocrit greater than 28% is necessary to maintain hemodynamic stability in patients with impaired ventricular function. In the present study, patients with hematocrit levels of 24% or greater were more likely to require transfusion in the operating room (p . 0.001). Therefore low hematocrit values (about 24%) are not necessarily a marker of increased risk in CABG patients, although it has predictive ability in other ICU patients.
This ICU risk stratification score relates to both morbidity and mortality as end points. Statistically, the contribution of patient management to outcome is more evident when the end point occurs more frequently. In this study the incidence of morbidity (10.4%) is higher than the incidence of overall mortality (3.1%). Therefore, morbidity better reflects the ICU and hospital length of stay and cost. Although many of the risk factors predict both morbidity and mortality, the logistic regression models demonstrate that some of the risk factors for mortality and morbidity were different. The clinical model included the most significant factors that encompassed both outcomes, and can be a useful tool for benchmark comparisons. This ICU admission model is part of a sequential evaluation of the probability of morbidity or mortality. In the present study, we investigated the sequential relationship of how the prognosis of mortality changes during the perioperative period (Fig 3). In patients with preoperative risk scores of 6 or less (n 5 2,118) the mortality was 2.3%; of these patients 97% had an ICU admission score of 14 or less with a mortality rate of 1.7%. The other 3% had an ICU admission score greater than 14 with a mortality of 19.4%. In the 318 patients with preoperative risk scores of 7 or greater the mortality was 9.4%. However 80% of these patients (n 5 254) had an ICU score of 14 or less and a mortality rate of 4.3%. The remaining 20% (n 5 64) of these patents were admitted to the ICU with a score greater than 14 and had a mortality rate of 29.7%. This sequential analysis demonstrates that the ICU
Fig 3. Sequential analysis of mortality risk in cardiac surgical patients. Use of preoperative and intensive care unit (ICU) risk stratification scores for sequential analysis of mortality risk. A preoperative score of 7 was used as the cut point between a low and high risk. An ICU admission score of 14 was used as the cut point to determine the level of risk.
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admission scoring model identifies patients with high risk of mortality based on postoperative status who were not identified by preoperative scoring alone. Thus discrimination of outcome based on ICU admission stratification is superior to stratification by preoperative status alone. This is borne out by comparison of receiver operating characteristic curves. In preoperative models for cardiac surgery, the area under the receiver operating characteristic curve (the C-statistic) for mortality prediction ranges from 0.74 to 0.83 [1, 4, 5, 17]. The comparable C-statistic for the ICU admission clinical model is 0.87. Similar results (a C-statistic of 0.85) have been reported for APACHE-III in the cardiac surgical setting [22]. However, caution should be exercised in comparing C-statistics across different populations in different studies, because C-statistics in independent, prospective validation studies are often lower [17]. Also, a prospective multicenter study for validation of this ICU model is necessary to establish a benchmark. It will also enhance the management strategies that improve outcome on the high-risk patients [9].
Limitations of the Study This study was performed in a single hospital with a high surgical volume and may not reflect the experience of hospitals performing a small number of CABG operations, as outcome can be related to surgical volume [24]. The model was developed and validated prospectively on large groups of patients; therefore, this score will be useful in comparing outcomes in groups of patients. The use of the model for risk prediction in individual patients can be limited. A logistic regression model cannot include every risk factor, especially factors with low incidence. In the present study, factors such as asystolic arrest, open chest on the day of operation, and tricuspid valve operation were highly significant univariate predictors of poor outcome but of such low prevalence (all ,0.8%) that they were not retained in the logistic regression models. There were too few outcomes to develop reliable univariate risk estimates for several predictive factors, such as aortic valve repair, ventricular aneurysm repair, or carotid repair performed concurrently with CABG. The simplification of continuous variables into scoring categories introduces large increments or steps of risk in the clinical model that contrasts with the gradual increases apparent in the logistic regression models. For example, creatinine clearance and renal reserve differ only slightly between a patient with a preoperative creatinine level of 1.9 mg/dL and one with a creatinine level of 1.8 mg/dL, but the clinical scoring system assigns increased risk to one and not the other.
Conclusions The ability to predict outcome with an ICU admission score enhances preoperative risk stratification and allows sequential prognosis, isolation of preoperative status from operative care in outcome assessment, and the ability to use a revised prognosis at ICU admission to plan further medical care and interventions.
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Morbidity and mortality in CABG patients are influenced by factors unique to the cardiac surgical patient, which may limit the applicability of general scoring systems to this specialized population. This ICU admission clinical model assesses the impact of comorbid disease, and includes factors that may not be adequately assessed in models derived from discharge or other administrative data [25–27]. The impact of ICU admission physiology and operative events should be considered in assessing postoperative outcome. Partial support for this study was provided by APACHE Medical Systems.
References 1. Higgins TL, Estafanous FG, Loop FD, et al. Stratification of morbidity and mortality outcome by preoperative risk factors in coronary artery bypass patients. JAMA 1992;267: 2344– 8. 2. Paiement B, Pelletier C, Dyrda I, et al. A simple classification of the risk in cardiac surgery. Can Anaesth Soc J 1983;30: 61– 8. 3. Hattler BG, Madia C, Johnson, et al. Risk stratification using The Society of Thoracic Surgeons program. Ann Thorac Surg 1994;58:1348–52. 4. Hannan EL, Kilburn H, O’Donnell JF, Lukacik G, Shields EP. Adult open heart surgery in New York State: an analysis of risk factors and hospital mortality rates. JAMA 1990;264: 2768–74. 5. O’Connor GT, Plume SK, Olmstead EM. Multivariate prediction of in-hospital mortality associated with coronary artery bypass graft surgery. Circulation 1992;85:2111– 8. 6. Kouchoukos NT, Oberman A, Kirlin JW, et al. Coronary bypass surgery: analysis of factors affecting hospital mortality. Circulation 1980;72(Suppl 1):84–9. 7. Merry AF, Ramage MC, Whitlock RML, et al. First-time coronary artery bypass grafting: the anaesthestist as a risk factor. Br J Anaesth 1992;68:6–12. 8. Cosgrove DM, Loop FD, Lytle BW, et al. Primary myocardial revascularization. Trends in surgical mortality. J Thorac Cardiovasc Surg 1984;88:673– 84. 9. Loop FD, Higgins TL, Panda R, et al. Myocardial protection during cardiac operations. J Thorac Cardiovasc Surg 1992; 104:608–14. 10. Slogoff S, Keats AS. Does perioperative myocardial ischemia lead to postoperative myocardial infarction? Anesthesiology 1985;62:107–14. 11. Higgins TL, Starr NJ. Risk stratification and outcome assessment of the adult cardiac surgical patient. Semin Thorac Cardiovasc Surg 1991;3:88–94. 12. Cleveland WS. Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc 1979;74:829–36. 13. Harrell FE Jr, Lee KL, Matchar DB, Reichert TA. Regression models for prognostic prediction: advantages, problems, and suggested solutions. Cancer Treatment Rep 1985;69:1071–7. 14. Hosmer DW, Lemeshow S. Goodness of fit tests for the multiple logistic regression model. Communication of Statistics: Theory and Methods 1980;A9:1043– 69. 15. Metz CE. Basic principles of ROC analysis. Semin Nucl Med 1978;8:283–93. 16. Metz CE, Wang PL, Kronman MB. A new approach for testing the significance of differences between ROC curves for correlated data. In: Decornick F, ed. Information processing in medical imaging. The Hague: Nijhoff, 1984. 17. Orr RK, Maini BS, Sottile FD, Dumas EM, O’Mara P. A comparison of four severity-adjusted models to predict mortality after coronary artery bypass graft surgery. Arch Surg 1995;130:301– 6.
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18. Pierpont GL, Kruse M, Ewald S, Weir EK. Practical problems in assessing risk for coronary artery bypass grafting. J Thorac Cardiovasc Surg 1985;89:673– 82. 19. Kennedy JW, Kaiser GC, Fisher LD, et al. Multivariate discriminant analysis of the clinical and angiographic predictors of operative mortality from the Collaborative Study in Coronary Artery Surgery (CASS). J Thorac Cardiovasc Surg 1980;80:876– 87. 20. Goenen M, Jacquemart JL, Galvez S, et al. Preoperative left ventricular dysfunction and operative risks in coronary bypass surgery. Chest 1987;92:804– 6. 21. Knaus WA, Wagner DP, Draper EA, et al. The APACHE III prognostic system: risk prediction of hospital mortality for critically ill hospitalized adults. Chest 1991;100:1619–23. 22. Becker RB, Zimmermann JE, Knaus WA, et al. The use of APACHE III to evaluate ICU length of stay, resource use,
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23. 24. 25. 26. 27.
and mortality after coronary artery bypass surgery. J Thorac Cardiovasc Surg 1995;36:1–11. Mossad E, Estafanous F. Blood use in cardiac surgery and the limitations of hemodilution. Curr Opin Cardiol 1995;10: 584–90. Flood AB, Scott WR, Ewy W. Does practice make perfect? Part I: The relation between hospital volume and outcomes for selected diagnostic categories. Med Care 1984;22:98 –114. Greenfield S, Aronow HU, Elashoff RM, et al. Flaws in mortality data. The hazards of ignoring comorbid disease. JAMA 1988;260:2253–5. Jencks SF, Williams DK, Kay TL. Assessing hospitalassociated deaths from discharge data. The role of length of stay and comorbidities. JAMA 1988;260:2240– 6. Jencks SF, Daley J, Draper D, Thomas N, Lenhart G, Walker J. Interpreting hospital mortality data. The role of clinical risk adjustment. JAMA 1988;260:3611– 6.
Notice From the American Board of Thoracic Surgery The American Board of Thoracic Surgery began its recertification process in 1984. Diplomates interested in participating in this examination should maintain a documented list of the operations they performed during the year prior to application for recertification. This practice review should consist of 1 year’s consecutive major operative experiences. (If more than 100 cases occur in 1 year, only 100 need to be listed.) They should also keep a record of their attendance at approved postgraduate medical education activities for the 2 years prior to application. A minimum of 100 hours of approved CME activity is required. In place of a cognitive examination, candidates for recertification will be required to complete both the general thoracic and cardiac portions of the SESATS VI syllabus (Self-Education/Self-Assessment in Thoracic Surgery). It is not necessary for candidates to purchase
© 1997 by The Society of Thoracic Surgeons Published by Elsevier Science Inc
SESATS VI booklets prior to applying for recertification. SESATS VI booklets will be forwarded to candidates after their applications have been accepted. Diplomates whose 10-year certificates will expire in 2000 may begin the recertification process in 1998. This new certificate will be dated 10 years from the time of expiration of the original certificate. Recertification is also open to any diplomate with an unlimited certificate and will in no way affect the validity of the original certificate. The deadline for submission of applications is May 1, 1998. A recertification brochure outlining the rules and requirements for recertification in thoracic surgery is available upon request from the American Board of Thoracic Surgery, One Rotary Center, Suite 803, Evanston, IL 60201.
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Background. This study was performed to develop an intensive care unit (ICU) admission risk score based on preoperative condition and intraoperative events. This score provides a tool with which to judge the effects of ICU quality of care on outcome. Methods. Data were collected prospectively on 4,918 patients (study group n 5 2,793 and a validation data set n 5 2,125) undergoing coronary artery bypass grafting alone or combined with a valve or carotid procedure between January 1, 1993, and March 31, 1995. Data were analyzed by univariate and multiple logistic regression with the end points of hospital mortality and serious ICU morbidity (stroke, low cardiac output, myocardial infarction, prolonged ventilation, serious infection, renal failure, or death).
Results. Eight risk factors predicted hospital mortality at ICU admission, and these factors and five others predicted morbidity. A clinical score, weighted equally for morbidity and mortality, was developed. All models fit according to the Hosmer-Lemeshow goodness-of-fit test. This score applies equally well to patients undergoing isolated coronary artery bypass grafting. Conclusions. This model is complementary to our previously reported preoperative model, allowing the process of ICU care to be measured independent of the operative care. Sequential scoring also allows updated prognoses at different points in the continuum of care.
O
Material and Methods
utcome after coronary artery bypass grafting (CABG) is determined by the preoperative status of the patient, as well as by technical factors in the operating room and intensive care unit (ICU). Preoperative risk stratification in the cardiac surgical population consistently identifies advanced age, emergency status, left ventricular dysfunction, a previous heart operation, diabetes, female sex, small body size, and renal disease as factors that affect outcome [1–5]. However, morbidity and mortality after CABG are also influenced by surgical and anesthetic techniques [6, 7], including time on cardiopulmonary bypass [7], completeness of revascularization [8], efficacy of myocardial protection [9], hemodynamic management [10], and unforeseen events in the operating room. The patient’s prognosis at arrival to the ICU may differ from the preoperative prognosis. In the present study, we evaluated the relative contribution of preoperative condition, operating room events, and physiologic measurements at ICU admission to outcome and describe a risk stratification score based on a patient’s status at ICU admission. Accepted for publication April 7, 1997. Address reprint requests to Dr Estafanous, Division of Anesthesiology and Critical Care, The Cleveland Clinic Foundation, 9500 Euclid Ave, Cleveland, OH 44195.
© 1997 by The Society of Thoracic Surgeons Published by Elsevier Science Inc
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We prospectively collected data on 4,918 consecutive adult patients undergoing CABG between January 1, 1993, and March 31, 1995. Preoperative data were collected by resident physicians, and ICU admission data were collected by trained physician assistants. Data collected during the first 15 months (n 5 2,793) were used to develop the model, and data collected during the next 12 months (n 5 2,125) were used to validate the model. Patients undergoing repeat operation, simultaneous carotid endarterectomy, or repair or replacement of the mitral or aortic valve (or both) were included in the study, because these “combined” CABG patients represent a large (27%) and growing part of our surgical population. A separate analysis was also conducted on the subset of patients (n 5 2,035; 73% of the developmental data set) undergoing isolated coronary revascularization. Six patients underwent both a primary and a repeat revascularization procedure during the study period, but only the first operation was considered for analysis. The reliability of data collection was assessed by comparing the data with chart review of a random subset. This review showed a relevant agreement of 98% to 100% of patients for each variable used in the final models. We chose more than 100 risk factors from the literature, our clinical experience, and our own work [1, 8, 11] and 0003-4975/97/$17.00 PII S0003-4975(97)00553-5
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examined the influence of each on outcome at discharge. Emergency cases were those characterized by unstable angina, unstable hemodynamics, or ischemic valvular dysfunction that could not be controlled medically. Left ventricular function was categorized as normal (ejection fraction [EF] $0.60], mildly impaired (EF 0.50 to 0.59), mildly to moderately impaired (EF 0.46 to 0.49), moderately impaired (EF 0.41 to 0.45), moderately to severely impaired (EF 0.36 to 0.40), or severely impaired (EF #0.35) based on the cardiologist’s evaluation of the ventriculogram at cardiac catheterization or by echocardiography if a ventriculogram was not recorded. Diabetes or chronic obstructive pulmonary disease was diagnosed only if the patient had a history of the condition and was maintained on appropriate medications. Valvular pathology was evaluated by stenosis versus regurgitation and repair versus replacement. Cardiopulmonary bypass time was the total of all bypass runs if a second or subsequent period of bypass was conducted. The need for a second or greater bypass run was also considered as a separate factor in the analysis. Morbidity was defined as the presence of one or more of the following during hospitalization: (1) cardiac complication: low cardiac output (sustained cardiac index #1.8 L z min21 z m22 despite adequate preload and inotropic support) or myocardial infarction documented by electrocardiography and enzyme criteria and that required the use of an intraaortic balloon pump (IABP) or a ventricular assist device (IABP and assist devices were resorted to when vasoactive drug support failed to achieve a cardiac index .2 L/m or mean arterial pressure .60 mm Hg without significant side effects (eg, persistent ischemia, arrhythmias); (2) prolonged ventilatory support: mechanical ventilation for 72 hours or more; (3) central nervous system complication: focal brain lesion confirmed by clinical findings, computed tomographic scan, or both, or diffuse encephalopathy with more than 24 hours of severely altered mental status, failure to awaken postoperatively, or both; (4) renal failure: urine output of ,400 mL/24 hours, institution of dialysis, or both; (5) serious infection: culture-proven pneumonia (blood or sputum), mediastinitis, wound infection, sepsis syndrome, or septic shock; or (6) death, because early hospital death might preclude the diagnosis of other morbidities. Mortality included all deaths during the hospitalization for the operation, regardless of length of stay.
Development of the Logistic Regression Model The association of each factor with morbidity and mortality was evaluated using a x2 test or Fisher’s exact test for categoric variables and Student’s t test for continuous variables. Because of the large number of variables analyzed, only the most significant are presented in Tables 1 and 2. Continuous variables significantly related to outcome were plotted using a locally weighted smoothing scatterplot technique [12] to assess linearity on the logit scale. If the relationship to risk was linear, the factor was entered into the model as a continuous variable. If the relationship to risk was not linear, appropriate
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cut points were determined from the locally weighted smoothing scatterplots to divide the distribution of values into categories. Locally weighted smoothing scatterplots were also used to divide variables that were continuous in the logistic models into categories for use in a clinical risk stratification score (see below). Factors considered for the multiple logistic regression included only those that were significant (p , 0.05) by univariate analysis, had a prevalence of at least 2% for categoric variables, and that indicated a patient condition rather than a therapeutic decision. Interactions of potentially related variables, such as low cardiac index and the use of an IABP, were also evaluated. The logistic regression analyses used a forward stepwise selection procedure with p values of 0.15 to enter and 0.05 to remain in the model. Two models were developed: one each for mortality and morbidity. The number of terms allowed in a logistic model was limited to 10% of the number of outcome events, with only the most significant factors included to avoid overfitting the models [13]. The goodness of fit of each final logistic model was evaluated using the Hosmer-Lemeshow x2 statistic [14]. In the event of a low number of expected events, the lowest deciles were combined for statistical analysis, with appropriate reduction in the degrees of freedom. Receiver operating characteristic curves were used to generate a C-statistic (area under the curve) to measure and compare the accuracy of the models [15, 16]. C-statistic values closer to 1.0 indicate better discrimination by the model.
Development of the Clinical Model and Risk Stratification Score A clinical model was created by first categorizing continuous predictors in the logistic regression models by using the locally weighted smoothing scatterplot procedure to identify cut points and then refitting the logistic models using all predictors as categoric variables. For example, for cardiopulmonary bypass time the cut point of 160 minutes was based on the much higher mortality above this value in the locally weighted smoothing scatterplots. Cut points were not based on being one or two standard deviations above the mean. Then a numeric score was calculated by summing the regression coefficient estimates of the logistic models for morbidity and mortality, multiplying by 2, and then rounding to the nearest integer to produce a clinical model that equally weights mortality and morbidity. This clinical model was evaluated for its goodness of fit and discrimination on the developmental data set, the subset of patients who underwent isolated coronary bypass without other simultaneous procedures, and the validation data set. Individual levels of morbidity and mortality risk were then combined into five levels of risk to facilitate the application of the scoring system when smaller numbers of patients are available, as might be seen in quarterly analysis or at institutions with smaller surgical volumes. The observed values in the validation set were then compared with the 95% confidence intervals of the predicted values generated from the developmental data set.
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Table 1. Selected Categoric Variables and Univariate Analysis Variable Sex (female) History of COPD/asthma History of pulmonary hypertension History of congestive heart failure History of dysrhythmias History of vascular disease or operation History of carotid disease or CVA Left main stenosis $70% Endocarditis Preop IABP Recent myocardial infarction (,3 mo) Diabetes mellitus (requiring medication) One prior heart operation Two or more prior heart operations Preop LVEF ,0.35 Emergency procedure One or two IMA grafts IABP after bypass AV replacement or repair MV replacement MV repair TV replacement or repair Aortic operation in addition to CABG OR HR .100 beats/min OR CI ,2.1 L z min21 z m22 Transfused more than 2 units in OR Urine output in OR ,500 mL More than one CPB run More than one cross-clamp ICU admit HR .100 beats/min ICU admit MAP ,60 mm Hg ICU admit CVP .17 mm Hg ICU admit PA diastolic .17 mm Hg ICU admit CI ,2.1 L z min21 z m22 ICU admit inotrope ICU admit pressor ICU admit antiarrhythmic ICU arrival MAP ,70 mm Hg
Mortality
Morbidity
Occurrence (%)
Odds Ratio
p Value
Odds Ratio
p Value
23.5 7.4 1.9 19.0 16.8 11.3 17.0 7.4 0.2 2.7 21.4 9.7 20.1 2.9 6.3 4.2 65.6 4.4 8.8 3.2 4.2 0.5 2.0 3.7 19.7 51.1 3.6 5.9 2.4 25.6 1.3 4.4 25.0 16.7 49.8 20.2 11.9 12.6
1.63 1.77 1.14 3.38 2.40 2.28 1.68 1.95 5.98 7.99 1.76 2.39 1.72 2.83 3.14 5.07 0.14 9.40 1.95 4.62 1.98 13.73 5.32 7.05 3.36 5.72 2.02 6.49 1.92 4.20 6.48 4.15 3.54 2.89 8.60 3.93 3.50 2.80
0.030 0.079 0.855 0.0001 0.0001 0.001 0.034 0.033 0.064 0.0001 0.013 0.001 0.022 0.014 0.0001 0.0001 0.0001 0.0001 0.023 0.0001 0.086 0.0001 0.0001 0.0001 0.0001 0.0001 0.100 0.0001 0.207 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
1.75 1.47 2.25 2.28 1.64 1.9 1.79 1.93 4.33 8.16 1.30 1.85 2.17 3.62 3.51 3.23 0.32 13.10 1.68 4.27 1.40 7.52 3.92 3.90 2.19 3.16 1.70 5.76 3.86 3.05 2.49 4.08 3.32 1.71 5.35 4.14 3.04 1.66
0.0001 0.068 0.015 0.0001 0.001 0.0001 0.0001 0.001 0.066 0.0001 0.067 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.006 0.0001 0.222 0.0001 0.0001 0.0001 0.0001 0.0001 0.057 0.0001 0.0001 0.0001 0.024 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.002
AV 5 atrial valve; CABG 5 coronary artery bypass grafting; CI 5 cardiac index; COPD 5 chronic obstructic pulmonary disease; CPB 5 cardiopulmonary bypass; CVA 5 cerebrovascular accident; CVP 5 central venous pressure; HR 5 heart rate; IABP 5 intraaortic balloon pump; ICU 5 intensive care unit; IMA 5 internal mammary artery; LVEF 5 left ventricular ejection fraction; MAP 5 mean arterial pressure; MV 5 mitral valve; PA 5 pulmonary artery pressure; OR 5 operating room; TV 5 tricuspid valve.
Most statistical analyses were performed with SAS version 6.0 (Statistical Analysis Systems, Cary, NC). The receiver operating characteristic analyses used programs developed by Metz and associates [15, 16].
Results Univariate risk factors for morbidity and mortality are presented in Table 1 (categoric variables) and Table 2 (continuous variables). Selected variables that were not statistically significant by univariate analysis included low urine output in the operating room, history of
chronic obstructive pulmonary disease requiring medication, history of renal failure requiring dialysis, chronic hypertension, race other than white, prior carotid operation, prior lung operation, and preoperative use of antiarrhythmics or calcium channel antagonists. Ventricular aneurysectomy or carotid operation in addition to CABG also did not achieve statistical significance. Fifty-eight variables qualified for entry into the multiple logistic regression analysis. The logistic regression models for mortality and morbidity are presented in Tables 3 and 4. These models were fit on 2,440 patients having complete data on these variables. To avoid over-
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Table 2. Selected Continuous Variables: Univariate Analysis Variable Age (y) Preop hematocrit (%) Preop creatinine (mg/dL) Preop albumin (mg/dL) Preop bilirubin (mg/dL) Height (cm) Weight (kg) Body surface area (Dubois method; m2) Preop arterial PCO2 (mm Hg) Preoperative cardiac index (L z min21 z m22) Cross clamp time (total minutes) CBP time (total minutes) Urine output in OR (mL z kg21 z h21) ICU arrival FIO2 ICU arrival pH (arterial blood gas, units) ICU arrival PCO2 (arterial blood gas, mm Hg) ICU arrival PO2 (arterial blood gas, mm Hg) ICU arrival HCO3 (arterial blood gas, mm Hg) ICU arrival core temp (degrees) ICU arrival HR (beats/min) ICU arrival MAP (mm Hg) ICU arrival CVP (mm Hg) ICU arrival PA systolic (mm Hg) ICU arrival PA diastolic (mm Hg) ICU arrival CI (L z min21 z m22) ICU arrival A-a O2 gradient (mm Hg) ICU hematocrit (%) ICU K1 (mEq/L) ICU glucose (mg/dL) ICU WBC (/mL)
Mean Value
SD
Mortality p Value
64.3 40.9 1.27 4.14 0.66 170.7 81.6 1.93 39.0 2.65 70.1 109.1 4.42 0.61 7.42 35.8 158.2 23.4 36.2 93.5 82.2 10.5 29.7 14.7 2.68 229.5 29.6 3.76 206.5 12,100.0
10.0 4.8 0.65 0.48 0.40 9.4 16.1 0.21 4.80 0.64 30.8 47.0 2.57 0.08 0.06 5.4 60.7 2.3 0.7 14.3 11.5 3.8 8.1 5.0 0.63 74.8 4.3 0.51 76.6 4,810.0
0.0001 0.0004 0.005 0.0001 0.005 0.038 0.0089 0.004 0.79 0.0001 0.0008 0.0001 0.011 0.0009 0.0001 0.51 0.82 0.0001 0.007 0.0001 0.0124 0.0001 0.0001 0.0001 0.0002 0.003 0.002 0.11 0.0001 0.027
Morbidity p Value 0.0001 0.0001 0.005 0.0001 0.008 0.0002 0.0001 0.0001 0.50 0.0001 0.0001 0.0001 0.035 0.0001 0.0001 0.0023 0.016 0.0001 0.11 0.0001 0.19 0.0001 0.0001 0.0001 0.006 0.0001 0.004 0.028 0.0001 0.003
A-a O2 gradient 5 alveolar-arterial oxygen gradient; CI 5 cardiac index; CPB 5 cardiopulmonary bypass; CVP 5 central venous HR 5 heart rate; ICU 5 intensive care unit; MAP 5 mean arterial pressure; OR 5 pressure; FiO2 5 fraction of inspired oxygen; PO2 5 oxygen tension; SD 5 standard deviation; operating room; PA 5 pulmonary artery pressure; PCO2 5 carbon dioxide tension; WBC 5 white blood cell count.
fitting, the mortality model was restricted to the first eight significant factors, because there were only 78 deaths. Significant factors that would have been entered
into the model without this restriction were central venous pressure and mean arterial pressure values at ICU admission, and a history of congestive heart failure.
Table 3. Intensive Care Unit Admission Mortality Model Variable Intercept Use of IABP After CPB Cardiac index at ICU admission Age per decade Preoperative serum albumin (mg/dL) History of peripheral vascular disease or interventions CPB time per 10 minutes HR .100 beats/min at ICU admission Arterial bicarbonate at ICU Admission (m/mol) a
Regression Coefficient
Standard Error
Odds Ratio
95% Confidence Interval
4.12 1.49 20.64a 0.39 21.74a 1.07 0.10 0.82 20.17a
2.10 0.35 0.22 0.15 0.24 0.31 0.02 0.27 0.05
... 4.46 0.53 1.49 0.18 2.91 1.11 2.27 0.85
... 2.23– 8.90 0.35– 0.80 1.11–2.00 0.11– 0.28 1.57–5.38 1.07–1.15 1.36 –3.85 0.76 – 0.94
Negative values in the regression coefficient imply that lower values of the variable increase mortality.
CPB 5 cardiopulmonary bypass;
HR 5 heart rate;
IABP 5 intraaortic balloon pump;
ICU 5 intensive care unit.
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Table 4. Intensive Care Unit Admission Morbidity Model Variable Intercept Body surface area (m2) .2 prior heart operations History of peripheral vascular disease or interventions Age per decade Preoperative serum creatinine (mg/dL) Preoperative serum albumin (mg/dL) CPB time per 10 minutes Use of IABP after CPB A-a gradient at ICU admission per 10 mm Hg HR .100 beats/min at ICU admission Cardiac index at ICU admission (L z min21 z m22) CVP at ICU admission (mm Hg) Arterial bicarbonate at ICU admission (mmol/L) a
Regression Coefficient
Standard Error
Odds Ratio
95% Confidence Interval
2.57 20.90* 0.80 0.72
1.53 0.38 0.32 0.20
... 0.41 2.23 2.06
... 0.19 – 0.86 1.18 – 4.22 1.39 –3.05
0.21 0.21 20.90* 0.07 1.96 0.04 0.49 20.47a 0.10 20.13a
0.09 0.08 0.15 0.02 0.25 0.01 0.16 0.13 0.02 0.03
1.23 1.24 0.41 1.07 7.11 1.04 1.64 0.63 1.10 0.88
1.04 –1.46 1.06 –1.44 0.30 – 0.55 1.03–1.12 4.34 –11.65 1.02–1.06 1.20 –2.23 0.49 – 0.80 1.06 –1.15 0.82– 0.93
Negative values in the regression coefficient imply that lower values of the variable increase morbidity.
A-a 5 alveolar-arterial; CPB 5 cardiopulmonary bypass; pump; ICU 5 intensive care unit.
CVP 5 central venous pressure;
No such limits applied to the morbidity model, which has 13 factors, including all eight factors in the mortality model, plus central venous pressure and alveolar–arterial gradient at ICU admission, preoperative serum creatinine level, reoperation status, and body surface area. The risk stratification score (Table 5) incorporates all factors affecting morbidity and mortality from the multiple logistic regression models, divides continuous variables into categories, and assigns integer point values of 1 to 7 on the basis of the logistic regression coefficients. A patient’s stratification score is obtained by adding the point values from Table 5, which can then be related to mortality (Fig 1) and morbidity (Fig 2). In the total population, the mortality rate was 3.1% and the morbidity rate was 10.4%, but rates vary by ICU risk stratification score. The categorization of risk levels was made on the basis of similar outcomes. For example, patients with a score less than 5 at ICU admission are at risk of less than 1% for mortality and less than 5% for morbidity. The risks of mortality and morbidity increase with higher scores (see Figs 1, 2). Receiver operating characteristic curves were generated for all models. The C-statistics (areas under the curves) reflecting mortality for the logistic regression mortality and clinical models were 0.86 and 0.87, respectively, which were not significantly different from each other. The C-statistics reflecting morbidity for the logistic regression morbidity and clinical model were 0.82 and 0.80, respectively, reflecting slightly better discrimination (p 5 0.02) for the logistic regression model. Applying the clinical model to patients undergoing isolated CABG generated a C-statistic of 0.87 for mortality and 0.82 for morbidity. Using the clinical model, observed outcomes in patients in the validation data set fell within the 95% confidence intervals predicted by the developmental data set (see Figs 1, 2). Applying the clinical model to
HR 5 heart rate;
IABP 5 intraaortic balloon
patients in the validation data set produced C-statistics of 0.85 for mortality and 0.82 for morbidity. HosmerLemeshow goodness of fit determined that all logistic and clinical models calibrate well. The accuracy and goodness of fit of both the logistic and clinical models were very similar indicating that the clinical severity score, although not a probability, can be useful in categorizing risks of mortality and morbidity in CABG patients.
Comment Preoperatively, patients undergoing open heart operations should be expected to be concerned about the possible outcome of the operation, including morbidity and mortality. Payors and health providers need similar information. Several preoperative risk status models have been developed to predict outcome after coronary artery operations [1–5]. These models all predict mortality, and possibly morbidity, with good accuracy but they also have shortcomings [17]. The patient’s physiologic response to the operation and anesthesia cannot always be predicted from preoperative information. Unexpected intraoperative events, management of anesthesia [10], efficacy of myocardial preservation [9], completeness of myocardial revascularization, and uneventful reoperation can neutralize or amplify preoperative risk. All these factors can affect the prognosis. Thus reevaluation and assessment of a patient’s condition at ICU admission complements the preoperative evaluation and allows for a proactive ICU management plan and better prediction of outcome. Previous work from our group [1] identified 13 preoperative risk factors for morbidity and mortality after CABG. Four of these factors are also significant in the ICU admission models: preoperative serum creatinine
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Table 5. Intensive Care Unit Risk Stratification Score Variable Preoperative Factors Small body size (BSA ,1.72 m2) Prior heart operation One Two or more History of operation or angioplasty for peripheral vascular disease Age $70 years Preoperative creatinine $1.9 mg/dL Preoperative albumin ,3.5 mg/dL Intraoperative factors CPB time $160 minutes Use of IABP after CPB ICU admission physiology A-a O2 gradient $ 250 mm Hg Heart rate $ 100 beats/min Cardiac index ,2.1 L z min21 z m22 CVP $17 mm Hg Arterial bicarbonate ,21 mmol/L
Value 1 1 2 3 3 4 5 3 7 2 3 3 4 4
A-a 5 alveolar-arterial; BSA 5 body surface area; CPB 5 cardiopulmonary bypass; CVP 5 central venous pressure; IABP 5 intraaortic balloon pump; ICU 5 intensive care unit.
level, age, prior cardiac operation, and a history of vascular disease. Data on serum albumin level, a significant factor in the ICU model, were not available for consideration in our preoperative risk model (Table 6). However, preoperative hematocrit, used previously as a measure of chronic disease, appears to account for some of the same infor-
Fig 1. Mortality by intensive care unit (ICU) admission risk group. Risk is estimated by summing the individual elements in Table 5. Results are displayed for the developmental set (1993–94) and the prospective validation set (1994 –95). All observed (validation) values fall within the 95% confidence intervals established from the developmental set.
Fig 2. Morbidity by intensive care unit (ICU) admission risk group. See legend for Figure 1.
mation as albumin (Pearson correlation coefficient between hematocrit and serum albumin 5 0.43). If serum albumin level is not considered as a variable, hematocrit would appear as a fifth factor common to the preoperative and ICU admission models. Several other factors in the preoperative risk score did not appear in the ICU admission score because these preoperative risk factors are managed during the operation or are subsumed by other scoring elements. For example, preoperative left ventricular dysfunction is an important preoperative risk factor [1–5, 18 –20] and it appears as a univariate risk factor in the present study, but it did not remain in the ICU admission logistic regression models. Preoperative ventricular dysfunction (EF , 0.35) is not associated with low cardiac index on arrival to ICU (p 5 0.50), but it is highly associated (p , 0.0001) with the use of IABP after bypass. The use of IABP after bypass thus becomes a better prognostic indicator than preoperative left ventricular dysfunction. This study also confirmed that intraoperative events such as cardiopulmonary bypass time and the use of an IABP are as important as preoperative risk factors in predicting outcome. Cardiopulmonary bypass time was analyzed as a continuous factor in the logistic model and it showed increase in mortality and morbidity with increasing time. We use 160 minutes as the cutpoint, which is 1.58 times the median time (101 minutes). The 160 minutes represents the 89.1 percentile and was chosen based on locally weighted smoothing scatterplot curve analysis demonstrating a sharp increase in poor outcome above this value. Emergency procedures are significant preoperative predictors of poor outcome, but were not significant in the ICU admission logistic regression models. Emergency patients are more likely to have other risk factors on ICU arrival such as low cardiac index, decreased serum albumin, higher alveolar–arterial oxygen gradient, elevated central venous pressure, and tachycardia. These
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Table 6. Preoperative Severity Score Variable Emergency Serum creatinine $1.9 mg% Serum creatinine 1.6 –1.8 mg% Severe LV dysfunction Prior cardiac operation Operative mitral valve insufficiency Age 75 years or older Age 65 to 74 years Prior vascular surgery COPD on medication Anemia (Hct #34%) Operative aortic stenosis Weight #65 kg Diabetes on medication Cerebrovascular disease Maximum score Clinically relevant range COPD 5 chronic obstructive pulmonary disease; LV 5 left ventricular.
Score 6 4 1 3 3 3 2 1 2 2 2 1 1 1 1 31 0 to 131 Hct 5 hematocrit;
Reprinted with permission from JAMA 1992;267:2345 (copyright 1992, American Medical Association).
were included in the logistic and clinical models. It is clear that scoring based on physiologic variables is more patient specific and is preferable to the term “emergency,” a definition that might vary between institutions. Several other physiologic measurements at ICU admission are predictors of outcome in this study, namely alveolar–arterial oxygen gradient, heart rate, cardiac index, central venous pressure, and arterial bicarbonate. The use of physiologic measurements to predict outcome was pioneered by the APACHE prognostic system [21] and successfully predicts mortality, resource use, and ICU length of stay in CABG patients [22]. The present study confirms the importance of physiologic measurements at ICU admission as outcome predictors. Some of the physiologic variables (such as body temperature and hematocrit) scored by APACHE-III, a benchmark for determining outcome in noncardiac intensive care units, may have a different relationship with outcome in CABG patients [21]. During cardiopulmonary bypass most patients are cooled systematically and their temperature at ICU admission can be less than 37°C. This does not reflect a pathologic status. Hemodilution is now a routine practice in cardiac surgery. Immediate postoperative hematocrit of 24% or greater is generally considered adequate. Experimental studies by our group [23] and our clinical experience demonstrated that hematocrit greater than 28% is necessary to maintain hemodynamic stability in patients with impaired ventricular function. In the present study, patients with hematocrit levels of 24% or greater were more likely to require transfusion in the operating room (p . 0.001). Therefore low hematocrit values (about 24%) are not necessarily a marker of increased risk in CABG patients, although it has predictive ability in other ICU patients.
This ICU risk stratification score relates to both morbidity and mortality as end points. Statistically, the contribution of patient management to outcome is more evident when the end point occurs more frequently. In this study the incidence of morbidity (10.4%) is higher than the incidence of overall mortality (3.1%). Therefore, morbidity better reflects the ICU and hospital length of stay and cost. Although many of the risk factors predict both morbidity and mortality, the logistic regression models demonstrate that some of the risk factors for mortality and morbidity were different. The clinical model included the most significant factors that encompassed both outcomes, and can be a useful tool for benchmark comparisons. This ICU admission model is part of a sequential evaluation of the probability of morbidity or mortality. In the present study, we investigated the sequential relationship of how the prognosis of mortality changes during the perioperative period (Fig 3). In patients with preoperative risk scores of 6 or less (n 5 2,118) the mortality was 2.3%; of these patients 97% had an ICU admission score of 14 or less with a mortality rate of 1.7%. The other 3% had an ICU admission score greater than 14 with a mortality of 19.4%. In the 318 patients with preoperative risk scores of 7 or greater the mortality was 9.4%. However 80% of these patients (n 5 254) had an ICU score of 14 or less and a mortality rate of 4.3%. The remaining 20% (n 5 64) of these patents were admitted to the ICU with a score greater than 14 and had a mortality rate of 29.7%. This sequential analysis demonstrates that the ICU
Fig 3. Sequential analysis of mortality risk in cardiac surgical patients. Use of preoperative and intensive care unit (ICU) risk stratification scores for sequential analysis of mortality risk. A preoperative score of 7 was used as the cut point between a low and high risk. An ICU admission score of 14 was used as the cut point to determine the level of risk.
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admission scoring model identifies patients with high risk of mortality based on postoperative status who were not identified by preoperative scoring alone. Thus discrimination of outcome based on ICU admission stratification is superior to stratification by preoperative status alone. This is borne out by comparison of receiver operating characteristic curves. In preoperative models for cardiac surgery, the area under the receiver operating characteristic curve (the C-statistic) for mortality prediction ranges from 0.74 to 0.83 [1, 4, 5, 17]. The comparable C-statistic for the ICU admission clinical model is 0.87. Similar results (a C-statistic of 0.85) have been reported for APACHE-III in the cardiac surgical setting [22]. However, caution should be exercised in comparing C-statistics across different populations in different studies, because C-statistics in independent, prospective validation studies are often lower [17]. Also, a prospective multicenter study for validation of this ICU model is necessary to establish a benchmark. It will also enhance the management strategies that improve outcome on the high-risk patients [9].
Limitations of the Study This study was performed in a single hospital with a high surgical volume and may not reflect the experience of hospitals performing a small number of CABG operations, as outcome can be related to surgical volume [24]. The model was developed and validated prospectively on large groups of patients; therefore, this score will be useful in comparing outcomes in groups of patients. The use of the model for risk prediction in individual patients can be limited. A logistic regression model cannot include every risk factor, especially factors with low incidence. In the present study, factors such as asystolic arrest, open chest on the day of operation, and tricuspid valve operation were highly significant univariate predictors of poor outcome but of such low prevalence (all ,0.8%) that they were not retained in the logistic regression models. There were too few outcomes to develop reliable univariate risk estimates for several predictive factors, such as aortic valve repair, ventricular aneurysm repair, or carotid repair performed concurrently with CABG. The simplification of continuous variables into scoring categories introduces large increments or steps of risk in the clinical model that contrasts with the gradual increases apparent in the logistic regression models. For example, creatinine clearance and renal reserve differ only slightly between a patient with a preoperative creatinine level of 1.9 mg/dL and one with a creatinine level of 1.8 mg/dL, but the clinical scoring system assigns increased risk to one and not the other.
Conclusions The ability to predict outcome with an ICU admission score enhances preoperative risk stratification and allows sequential prognosis, isolation of preoperative status from operative care in outcome assessment, and the ability to use a revised prognosis at ICU admission to plan further medical care and interventions.
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Morbidity and mortality in CABG patients are influenced by factors unique to the cardiac surgical patient, which may limit the applicability of general scoring systems to this specialized population. This ICU admission clinical model assesses the impact of comorbid disease, and includes factors that may not be adequately assessed in models derived from discharge or other administrative data [25–27]. The impact of ICU admission physiology and operative events should be considered in assessing postoperative outcome. Partial support for this study was provided by APACHE Medical Systems.
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18. Pierpont GL, Kruse M, Ewald S, Weir EK. Practical problems in assessing risk for coronary artery bypass grafting. J Thorac Cardiovasc Surg 1985;89:673– 82. 19. Kennedy JW, Kaiser GC, Fisher LD, et al. Multivariate discriminant analysis of the clinical and angiographic predictors of operative mortality from the Collaborative Study in Coronary Artery Surgery (CASS). J Thorac Cardiovasc Surg 1980;80:876– 87. 20. Goenen M, Jacquemart JL, Galvez S, et al. Preoperative left ventricular dysfunction and operative risks in coronary bypass surgery. Chest 1987;92:804– 6. 21. Knaus WA, Wagner DP, Draper EA, et al. The APACHE III prognostic system: risk prediction of hospital mortality for critically ill hospitalized adults. Chest 1991;100:1619–23. 22. Becker RB, Zimmermann JE, Knaus WA, et al. The use of APACHE III to evaluate ICU length of stay, resource use,
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Notice From the American Board of Thoracic Surgery The American Board of Thoracic Surgery began its recertification process in 1984. Diplomates interested in participating in this examination should maintain a documented list of the operations they performed during the year prior to application for recertification. This practice review should consist of 1 year’s consecutive major operative experiences. (If more than 100 cases occur in 1 year, only 100 need to be listed.) They should also keep a record of their attendance at approved postgraduate medical education activities for the 2 years prior to application. A minimum of 100 hours of approved CME activity is required. In place of a cognitive examination, candidates for recertification will be required to complete both the general thoracic and cardiac portions of the SESATS VI syllabus (Self-Education/Self-Assessment in Thoracic Surgery). It is not necessary for candidates to purchase
© 1997 by The Society of Thoracic Surgeons Published by Elsevier Science Inc
SESATS VI booklets prior to applying for recertification. SESATS VI booklets will be forwarded to candidates after their applications have been accepted. Diplomates whose 10-year certificates will expire in 2000 may begin the recertification process in 1998. This new certificate will be dated 10 years from the time of expiration of the original certificate. Recertification is also open to any diplomate with an unlimited certificate and will in no way affect the validity of the original certificate. The deadline for submission of applications is May 1, 1998. A recertification brochure outlining the rules and requirements for recertification in thoracic surgery is available upon request from the American Board of Thoracic Surgery, One Rotary Center, Suite 803, Evanston, IL 60201.
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